code: def test_generation_is_the_same_as_for_causal_language_modeling(df):
      """
      DPO model should generate the same output text as causal language modeling
      """
      device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
      generated_text_causal_lm = generate_causal_lm_model_text(df)
  
      cfg = ConfigProblemBase(
          llm_backbone="MaxJeblick/llama2-0b-unit-test",
          dataset=ConfigDPODataset(
              system_column="system",
              prompt_column=("prompt",),
              answer_column="answer_column",
              rejected_answer_column="rejected_answer",
          ),
          tokenizer=ConfigNLPCausalLMTokenizer(
              max_length_prompt=256,
              max_length_answer=256,
              max_length=512,
          ),
      )
      cfg.architecture.backbone_dtype = "float32"
  
      dataset = CustomDataset(df, cfg, mode="train")
      model = Model(cfg).eval().to(device)
      dataloader = torch.utils.data.DataLoader(
          dataset, batch_size=16, shuffle=True
      )
  
      batch = next(iter(dataloader))
      batch = {k: v.to(device) for k, v in batch.items()}
      batch_padding(
          cfg,
          batch,
          mask_key="prompt_attention_mask",
          pad_keys=[
              "prompt_input_ids",
              "prompt_attention_mask",
              "prompt_special_tokens_mask",
          ],
      )
      with torch.no_grad():
          generated_text = dataset.tokenizer.decode(
              model.generate(batch, cfg)[0]
          )
  
      assert generated_text == generated_text_causal_lm, (
          "Generated text is not the same as from causal LM model:"
          "{}\n{}".format(generated_text, generated_text_causal_lm)
      )
  
file_path: h2o-llmstudio/tests/src/models/test_dpo_modeling_model.py
imports: import pandas
  import pytest
  import torch
  from llm_studio.python_configs.text_causal_language_modeling_config import ConfigNLPCausalLMTokenizer
  from llm_studio.python_configs.text_dpo_modeling_config import ConfigDPODataset, ConfigProblemBase
  from llm_studio.src.datasets.text_dpo_modeling_ds import CustomDataset
  from llm_studio.src.models.text_dpo_modeling_model import Model
  from llm_studio.src.utils.data_utils import batch_padding
label_desc: Use image node_id 3 for calling a global function with example usage: test_generation_is_the_same_as_for_causal_language_modeling(df) without return types